a Df@sdZddlZddlZddlZddlZddlmZddlmZddl m Z m Z m Z e dZ ddZee d dd d ZdddZdS)z AutoAnchor utils N)tqdm) TryExcept)LOGGERTQDM_BAR_FORMATcolorstrz AutoAnchor: cCsz|jddd}|d|d}|jd|jd}|rv||krvttd|j d|jdd<dS)NrzReversing anchor order) anchorsprodmeanviewstridesignrinfoPREFIXflip)madadsrF/data/NAS7/SABIOD/METHODE/ermites/yolo-dyni/yolov5/utils/autoanchor.pycheck_anchor_orders rERROR@cst|dr|jjdn|jd}||j|jjddd}tjjdd|jddfd }t t d d t |||j Dfd d }|j|jjddd}|j|} || dd\} } dt| dd| dd} | dkrt| dnt| d|jd} t|| |ddd} || d}|| krt j | |jjd|j} | |j|jdd<t||j|_td} n td} t| dS)NmodulerTkeepdims?g?r)sizecSs(g|] \}}|ddddf|qSNr.0slrrr #z!check_anchors..csvdddf|d}t|d|dd}|dd}|dkd}|dk}||fSNrr)torchminmaxfloatsumr )krxbestaatbprthrwhrrmetric%s zcheck_anchors..metricr+ .2fz anchors/target, .3fz Best Possible Recall (BPR). g\(\?u-Current anchors are a good fit to dataset ✅uBAnchors are a poor fit to dataset ⚠️, attempting to improve...F)nimg_sizer8genverbose)deviceuKDone ✅ (optional: update model *.yaml to use these anchors in the future)uXDone ⚠️ (original anchors better than new anchors, proceeding with original anchors))hasattrrmodelshapesr.nprandomuniformshaper,tensor concatenateziplabelsr/r torrCr clonecpurrrnumel kmean_anchorstype_asview_asr)datasetrEr8imgszrrFscaler:r rr6r5r&naZnew_bprrr7r check_anchorss. *     rZ./data/coco128.yaml r>Tc sddlm}tj}dddfdd}d+fd d } t|trt|d d } t| } Wd n1s~0Yddl m } | | dddd}|j |j j ddd} t ddt| |jDdkd}|rttd|dtddkdtjzlttddtdtksbJd}||ddd|}t|ksJWnDtyttdt|dd}Yn0d d!fD\| |d"d#}|||jd$d%f\} }}}tt |t!d&}|D]}t"|}|dk#r|||kt|j$||d%d'd}q>|&|j%dd(}||}|| kr0||&} }td)| d*|_'|r0| ||q0| |tjS),aB Creates kmeans-evolved anchors from training dataset Arguments: dataset: path to data.yaml, or a loaded dataset n: number of anchors img_size: image size used for training thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 gen: generations to evolve anchors using genetic algorithm verbose: print all results Return: k: kmeans evolved anchors Usage: from utils.autoanchor import *; _ = kmean_anchors() r)kmeansrcSsD|dddf|d}t|d|dd}||ddfSr*)r,r-r.)r1r9r2r3rrrr:Yszkmean_anchors..metriccs.tj|tjd\}}||kS)Ndtype)r,rKfloat32r/r )r1_r4)r:r8r9rranchor_fitness_sz%kmean_anchors..anchor_fitnessTcs|t|d}|\}}|k|k}}tddd|dd|ddtdd d |d d |d d ||kd d}|D]$}|dt|dt|df7}q|rt|dd|S)Nrzthr=r<z: .4fz best possible recall, z anchors past thr zn=z , img_size=z , metric_all=r=/z-mean/best, past_thr=z-mean: z%i,%i, r) rGargsortr r/r rroundrr)r1rBr3r4r6r5r&)r@r:r?r8wh0rr print_resultscs*&"z$kmean_anchors..print_resultsignore)errorsN)LoadImagesAndLabelstrain)augmentrectrcSs(g|] \}}|ddddf|qSr!rr$rrrr(xr)z!kmean_anchors..g@u.WARNING ⚠️ Extremely small objects found: z of z labels are <3 pixels in sizeg@zRunning kmeans for z anchors on z points...)iteru>WARNING ⚠️ switching strategies from kmeans to random initr+css|]}tj|tjdVqdS)r^N)r,rKr`)r%r3rrr r)z kmean_anchors..F)rBrg?) bar_formatg333333?)r-z3Evolving anchors with Genetic Algorithm: fitness = rc)T)(Zscipy.cluster.vqr]rGrH isinstancestropenyaml safe_loadutils.dataloadersrlrFr.rLrMrNanyr0rrrlenastyper`std ExceptionwarningsortrandreshaperJrrangeronesallrandnclipcopydesc)rVr?r@r8rArBr]nprrbrif data_dictrlrFir&r1shmppbarravkgfgr)r@r:r?r8r9rhrrSCsT  (    &  4 rS)rr)r[r\rrr>T)__doc__rHnumpyrGr,rwrutilsr utils.generalrrrrrrZrSrrrrs    %